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An adaptive ant colony system algorithm for continuous-space optimization problems

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Abstract

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

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Authors and Affiliations

  1. Institute of Intelligent Systems and Decision Making, Zhejiang University, 310027, Hangzhou, China

    Li Yan-jun & Wu Tie-jun

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  1. Li Yan-jun
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  2. Wu Tie-jun
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Correspondence to Li Yan-jun.

Additional information

Project (No. 9845-005) supported by National High-Tech. Research & Development Plan, China

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Yan-jun, L., Tie-jun, W. An adaptive ant colony system algorithm for continuous-space optimization problems. J. Zheijang Univ.-Sci. 4, 40–46 (2003). https://doi.org/10.1631/BF02841077

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  • DOI: https://doi.org/10.1631/BF02841077

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